Overview

Dataset statistics

Number of variables19
Number of observations101774
Missing cells1221
Missing cells (%)0.1%
Duplicate rows541
Duplicate rows (%)0.5%
Total size in memory15.5 MiB
Average record size in memory160.0 B

Variable types

Numeric10
Text3
Categorical5
Boolean1

Alerts

Dataset has 541 (0.5%) duplicate rowsDuplicates
availability 365 has 23902 (23.5%) zerosZeros

Reproduction

Analysis started2024-11-13 10:31:10.838901
Analysis finished2024-11-13 10:31:34.983539
Duration24.14 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct101233
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29223239
Minimum1001254
Maximum57367417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:35.141939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1001254
5-th percentile3955947.8
Q115167969
median29284343
Q343279211
95-th percentile54538178
Maximum57367417
Range56366163
Interquartile range (IQR)28111242

Descriptive statistics

Standard deviation16236664
Coefficient of variation (CV)0.55560794
Kurtosis-1.201953
Mean29223239
Median Absolute Deviation (MAD)14055759
Skewness0.0018447284
Sum2.9741659 × 1012
Variance2.6362924 × 1014
MonotonicityNot monotonic
2024-11-13T10:31:35.359740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35566479 2
 
< 0.1%
6035551 2
 
< 0.1%
6039969 2
 
< 0.1%
6039417 2
 
< 0.1%
6038864 2
 
< 0.1%
6038312 2
 
< 0.1%
6037760 2
 
< 0.1%
6037207 2
 
< 0.1%
6036655 2
 
< 0.1%
6036103 2
 
< 0.1%
Other values (101223) 101754
> 99.9%
ValueCountFrequency (%)
1001254 1
< 0.1%
1002102 1
< 0.1%
1002403 1
< 0.1%
1002755 1
< 0.1%
1003689 1
< 0.1%
1004098 1
< 0.1%
1004650 1
< 0.1%
1005202 1
< 0.1%
1005754 1
< 0.1%
1006307 1
< 0.1%
ValueCountFrequency (%)
57367417 1
< 0.1%
57366865 1
< 0.1%
57366313 1
< 0.1%
57365760 1
< 0.1%
57365208 1
< 0.1%
57364656 1
< 0.1%
57364103 1
< 0.1%
57360237 1
< 0.1%
57359685 1
< 0.1%
57359133 1
< 0.1%

NAME
Text

Distinct60794
Distinct (%)59.9%
Missing220
Missing (%)0.2%
Memory size1.6 MiB
2024-11-13T10:31:36.088857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length248
Median length147
Mean length37.578441
Min length1

Characters and Unicode

Total characters3816241
Distinct characters988
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32209 ?
Unique (%)31.7%

Sample

1st rowClean & quiet apt home by the park
2nd rowSkylit Midtown Castle
3rd rowTHE VILLAGE OF HARLEM....NEW YORK !
4th rowEntire Apt: Spacious Studio/Loft by central park
5th rowLarge Cozy 1 BR Apartment In Midtown East
ValueCountFrequency (%)
in 34580
 
5.5%
room 21135
 
3.3%
17782
 
2.8%
bedroom 15998
 
2.5%
private 15423
 
2.4%
apartment 13993
 
2.2%
cozy 10495
 
1.7%
apt 9290
 
1.5%
studio 8600
 
1.4%
brooklyn 8540
 
1.4%
Other values (15933) 475207
75.3%
2024-11-13T10:31:37.029634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
532797
 
14.0%
e 266335
 
7.0%
o 260392
 
6.8%
t 223622
 
5.9%
a 218795
 
5.7%
r 207085
 
5.4%
n 199709
 
5.2%
i 199480
 
5.2%
l 108586
 
2.8%
m 103752
 
2.7%
Other values (978) 1495688
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3816241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
532797
 
14.0%
e 266335
 
7.0%
o 260392
 
6.8%
t 223622
 
5.9%
a 218795
 
5.7%
r 207085
 
5.4%
n 199709
 
5.2%
i 199480
 
5.2%
l 108586
 
2.8%
m 103752
 
2.7%
Other values (978) 1495688
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3816241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
532797
 
14.0%
e 266335
 
7.0%
o 260392
 
6.8%
t 223622
 
5.9%
a 218795
 
5.7%
r 207085
 
5.4%
n 199709
 
5.2%
i 199480
 
5.2%
l 108586
 
2.8%
m 103752
 
2.7%
Other values (978) 1495688
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3816241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
532797
 
14.0%
e 266335
 
7.0%
o 260392
 
6.8%
t 223622
 
5.9%
a 218795
 
5.7%
r 207085
 
5.4%
n 199709
 
5.2%
i 199480
 
5.2%
l 108586
 
2.8%
m 103752
 
2.7%
Other values (978) 1495688
39.2%

host id
Real number (ℝ)

Distinct101232
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9240105 × 1010
Minimum1.2360052 × 108
Maximum9.8763129 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:37.270618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.2360052 × 108
5-th percentile4.8993393 × 109
Q12.4561689 × 1010
median4.9099941 × 1010
Q37.3975864 × 1010
95-th percentile9.3816172 × 1010
Maximum9.8763129 × 1010
Range9.8639529 × 1010
Interquartile range (IQR)4.9414175 × 1010

Descriptive statistics

Standard deviation2.8541121 × 1010
Coefficient of variation (CV)0.57963161
Kurtosis-1.2021264
Mean4.9240105 × 1010
Median Absolute Deviation (MAD)2.4718743 × 1010
Skewness0.0067624612
Sum5.0113624 × 1015
Variance8.1459561 × 1020
MonotonicityNot monotonic
2024-11-13T10:31:37.469852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.81726092 × 10102
 
< 0.1%
7.100281958 × 10102
 
< 0.1%
6634910359 2
 
< 0.1%
5.14287728 × 10102
 
< 0.1%
7.48016913 × 10102
 
< 0.1%
4.815386532 × 10102
 
< 0.1%
7565053705 2
 
< 0.1%
2500204289 2
 
< 0.1%
2262501486 2
 
< 0.1%
6.833789892 × 10102
 
< 0.1%
Other values (101222) 101754
> 99.9%
ValueCountFrequency (%)
123600518 1
< 0.1%
124039648 1
< 0.1%
124472619 1
< 0.1%
129756565 1
< 0.1%
130349612 1
< 0.1%
130593431 1
< 0.1%
131602089 1
< 0.1%
132238305 1
< 0.1%
133264740 1
< 0.1%
134452120 1
< 0.1%
ValueCountFrequency (%)
9.876312902 × 10101
< 0.1%
9.876268323 × 10101
< 0.1%
9.876266181 × 10101
< 0.1%
9.876096863 × 10101
< 0.1%
9.875813627 × 10101
< 0.1%
9.875797561 × 10101
< 0.1%
9.875795011 × 10101
< 0.1%
9.875764603 × 10101
< 0.1%
9.87574594 × 10101
< 0.1%
9.875733136 × 10101
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
unconfirmed
51074 
verified
50700 

Length

Max length11
Median length11
Mean length9.5055122
Min length8

Characters and Unicode

Total characters967414
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunconfirmed
2nd rowverified
3rd rowunconfirmed
4th rowunconfirmed
5th rowverified

Common Values

ValueCountFrequency (%)
unconfirmed 51074
50.2%
verified 50700
49.8%

Length

2024-11-13T10:31:37.676621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T10:31:37.835174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
unconfirmed 51074
50.2%
verified 50700
49.8%

Most occurring characters

ValueCountFrequency (%)
i 152474
15.8%
e 152474
15.8%
n 102148
10.6%
f 101774
10.5%
r 101774
10.5%
d 101774
10.5%
u 51074
 
5.3%
c 51074
 
5.3%
o 51074
 
5.3%
m 51074
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 967414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 152474
15.8%
e 152474
15.8%
n 102148
10.6%
f 101774
10.5%
r 101774
10.5%
d 101774
10.5%
u 51074
 
5.3%
c 51074
 
5.3%
o 51074
 
5.3%
m 51074
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 967414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 152474
15.8%
e 152474
15.8%
n 102148
10.6%
f 101774
10.5%
r 101774
10.5%
d 101774
10.5%
u 51074
 
5.3%
c 51074
 
5.3%
o 51074
 
5.3%
m 51074
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 967414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 152474
15.8%
e 152474
15.8%
n 102148
10.6%
f 101774
10.5%
r 101774
10.5%
d 101774
10.5%
u 51074
 
5.3%
c 51074
 
5.3%
o 51074
 
5.3%
m 51074
 
5.3%
Distinct13148
Distinct (%)13.0%
Missing386
Missing (%)0.4%
Memory size1.6 MiB
2024-11-13T10:31:38.484377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length32
Mean length6.1737089
Min length1

Characters and Unicode

Total characters625940
Distinct characters215
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3933 ?
Unique (%)3.9%

Sample

1st rowMadaline
2nd rowJenna
3rd rowElise
4th rowGarry
5th rowLyndon
ValueCountFrequency (%)
2305
 
2.0%
and 1376
 
1.2%
michael 959
 
0.8%
david 846
 
0.7%
sonder 733
 
0.6%
alex 666
 
0.6%
john 659
 
0.6%
nyc 545
 
0.5%
laura 538
 
0.5%
daniel 511
 
0.4%
Other values (11677) 104694
92.0%
2024-11-13T10:31:39.392688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 78632
 
12.6%
e 59900
 
9.6%
i 50869
 
8.1%
n 50278
 
8.0%
r 37308
 
6.0%
l 32079
 
5.1%
o 27018
 
4.3%
t 20080
 
3.2%
s 19348
 
3.1%
h 18988
 
3.0%
Other values (205) 231440
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 625940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 78632
 
12.6%
e 59900
 
9.6%
i 50869
 
8.1%
n 50278
 
8.0%
r 37308
 
6.0%
l 32079
 
5.1%
o 27018
 
4.3%
t 20080
 
3.2%
s 19348
 
3.1%
h 18988
 
3.0%
Other values (205) 231440
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 625940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 78632
 
12.6%
e 59900
 
9.6%
i 50869
 
8.1%
n 50278
 
8.0%
r 37308
 
6.0%
l 32079
 
5.1%
o 27018
 
4.3%
t 20080
 
3.2%
s 19348
 
3.1%
h 18988
 
3.0%
Other values (205) 231440
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 625940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 78632
 
12.6%
e 59900
 
9.6%
i 50869
 
8.1%
n 50278
 
8.0%
r 37308
 
6.0%
l 32079
 
5.1%
o 27018
 
4.3%
t 20080
 
3.2%
s 19348
 
3.1%
h 18988
 
3.0%
Other values (205) 231440
37.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Manhattan
43471 
Brooklyn
41503 
Queens
13162 
Bronx
 
2688
Staten Island
 
950

Length

Max length13
Median length9
Mean length8.1359188
Min length5

Characters and Unicode

Total characters828025
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowManhattan
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 43471
42.7%
Brooklyn 41503
40.8%
Queens 13162
 
12.9%
Bronx 2688
 
2.6%
Staten Island 950
 
0.9%

Length

2024-11-13T10:31:39.616034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T10:31:39.792335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 43471
42.3%
brooklyn 41503
40.4%
queens 13162
 
12.8%
bronx 2688
 
2.6%
staten 950
 
0.9%
island 950
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n 146195
17.7%
a 132313
16.0%
t 88842
10.7%
o 85694
10.3%
B 44191
 
5.3%
r 44191
 
5.3%
M 43471
 
5.2%
h 43471
 
5.2%
l 42453
 
5.1%
y 41503
 
5.0%
Other values (10) 115701
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 828025
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 146195
17.7%
a 132313
16.0%
t 88842
10.7%
o 85694
10.3%
B 44191
 
5.3%
r 44191
 
5.3%
M 43471
 
5.2%
h 43471
 
5.2%
l 42453
 
5.1%
y 41503
 
5.0%
Other values (10) 115701
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 828025
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 146195
17.7%
a 132313
16.0%
t 88842
10.7%
o 85694
10.3%
B 44191
 
5.3%
r 44191
 
5.3%
M 43471
 
5.2%
h 43471
 
5.2%
l 42453
 
5.1%
y 41503
 
5.0%
Other values (10) 115701
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 828025
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 146195
17.7%
a 132313
16.0%
t 88842
10.7%
o 85694
10.3%
B 44191
 
5.3%
r 44191
 
5.3%
M 43471
 
5.2%
h 43471
 
5.2%
l 42453
 
5.1%
y 41503
 
5.0%
Other values (10) 115701
14.0%
Distinct224
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:40.344823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length26
Median length18
Mean length11.87371
Min length4

Characters and Unicode

Total characters1208435
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKensington
2nd rowMidtown
3rd rowHarlem
4th rowClinton Hill
5th rowEast Harlem
ValueCountFrequency (%)
east 13664
 
8.3%
side 9436
 
5.7%
bedford-stuyvesant 7877
 
4.8%
harlem 7730
 
4.7%
williamsburg 7709
 
4.7%
upper 7507
 
4.6%
heights 7295
 
4.4%
village 6003
 
3.7%
west 5384
 
3.3%
bushwick 4937
 
3.0%
Other values (236) 86667
52.8%
2024-11-13T10:31:41.208333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 110571
 
9.1%
i 86067
 
7.1%
s 82546
 
6.8%
t 80361
 
6.7%
a 79009
 
6.5%
l 70116
 
5.8%
r 69929
 
5.8%
62435
 
5.2%
n 54785
 
4.5%
o 50935
 
4.2%
Other values (44) 461681
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1208435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 110571
 
9.1%
i 86067
 
7.1%
s 82546
 
6.8%
t 80361
 
6.7%
a 79009
 
6.5%
l 70116
 
5.8%
r 69929
 
5.8%
62435
 
5.2%
n 54785
 
4.5%
o 50935
 
4.2%
Other values (44) 461681
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1208435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 110571
 
9.1%
i 86067
 
7.1%
s 82546
 
6.8%
t 80361
 
6.7%
a 79009
 
6.5%
l 70116
 
5.8%
r 69929
 
5.8%
62435
 
5.2%
n 54785
 
4.5%
o 50935
 
4.2%
Other values (44) 461681
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1208435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 110571
 
9.1%
i 86067
 
7.1%
s 82546
 
6.8%
t 80361
 
6.7%
a 79009
 
6.5%
l 70116
 
5.8%
r 69929
 
5.8%
62435
 
5.2%
n 54785
 
4.5%
o 50935
 
4.2%
Other values (44) 461681
38.2%

lat
Real number (ℝ)

Distinct21948
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728095
Minimum40.49979
Maximum40.91697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:41.497188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum40.49979
5-th percentile40.64322
Q140.68873
median40.72228
Q340.76279
95-th percentile40.82669
Maximum40.91697
Range0.41718
Interquartile range (IQR)0.07406

Descriptive statistics

Standard deviation0.055883344
Coefficient of variation (CV)0.001372108
Kurtosis0.14695938
Mean40.728095
Median Absolute Deviation (MAD)0.03674
Skewness0.2302758
Sum4145061.1
Variance0.0031229482
MonotonicityNot monotonic
2024-11-13T10:31:41.967857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.76411 36
 
< 0.1%
40.71813 31
 
< 0.1%
40.73756 27
 
< 0.1%
40.76125 27
 
< 0.1%
40.7244 25
 
< 0.1%
40.71353 25
 
< 0.1%
40.76106 25
 
< 0.1%
40.6898 24
 
< 0.1%
40.69175 23
 
< 0.1%
40.76189 23
 
< 0.1%
Other values (21938) 101508
99.7%
ValueCountFrequency (%)
40.49979 1
< 0.1%
40.50456 1
< 0.1%
40.50641 1
< 0.1%
40.50708 2
< 0.1%
40.50863 1
< 0.1%
40.50868 2
< 0.1%
40.50873 2
< 0.1%
40.50943 1
< 0.1%
40.51133 2
< 0.1%
40.52211 2
< 0.1%
ValueCountFrequency (%)
40.91697 1
< 0.1%
40.91685 1
< 0.1%
40.9131 1
< 0.1%
40.91306 1
< 0.1%
40.91248 1
< 0.1%
40.91234 1
< 0.1%
40.91169 2
< 0.1%
40.91167 1
< 0.1%
40.9116 1
< 0.1%
40.91139 1
< 0.1%

long
Real number (ℝ)

Distinct17726
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.94964
Minimum-74.24984
Maximum-73.70522
Zeros0
Zeros (%)0.0%
Negative101774
Negative (%)100.0%
Memory size1.6 MiB
2024-11-13T10:31:42.230602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-74.24984
5-th percentile-74.00386
Q1-73.98258
median-73.954445
Q3-73.93235
95-th percentile-73.85113
Maximum-73.70522
Range0.54462
Interquartile range (IQR)0.05023

Descriptive statistics

Standard deviation0.049551641
Coefficient of variation (CV)-0.00067007278
Kurtosis4.3376231
Mean-73.94964
Median Absolute Deviation (MAD)0.025945
Skewness1.2417649
Sum-7526150.6
Variance0.0024553651
MonotonicityNot monotonic
2024-11-13T10:31:42.530112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.99371 44
 
< 0.1%
-73.9535 40
 
< 0.1%
-73.95427 37
 
< 0.1%
-73.94791 37
 
< 0.1%
-73.94977 34
 
< 0.1%
-73.95677 34
 
< 0.1%
-73.94513 32
 
< 0.1%
-73.95675 32
 
< 0.1%
-73.943 31
 
< 0.1%
-73.95205 31
 
< 0.1%
Other values (17716) 101422
99.7%
ValueCountFrequency (%)
-74.24984 1
< 0.1%
-74.24442 1
< 0.1%
-74.24285 2
< 0.1%
-74.24135 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 2
< 0.1%
-74.23914 2
< 0.1%
-74.23803 2
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
ValueCountFrequency (%)
-73.70522 1
 
< 0.1%
-73.70524 1
 
< 0.1%
-73.71087 1
 
< 0.1%
-73.71299 3
< 0.1%
-73.7169 1
 
< 0.1%
-73.71795 1
 
< 0.1%
-73.71829 1
 
< 0.1%
-73.71928 1
 
< 0.1%
-73.71997 1
 
< 0.1%
-73.72122 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.5 KiB
False
51121 
True
50653 
ValueCountFrequency (%)
False 51121
50.2%
True 50653
49.8%
2024-11-13T10:31:42.756929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
moderate
34093 
strict
33856 
flexible
33825 

Length

Max length8
Median length8
Mean length7.3346827
Min length6

Characters and Unicode

Total characters746480
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstrict
2nd rowmoderate
3rd rowflexible
4th rowmoderate
5th rowmoderate

Common Values

ValueCountFrequency (%)
moderate 34093
33.5%
strict 33856
33.3%
flexible 33825
33.2%

Length

2024-11-13T10:31:42.994636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T10:31:43.206274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
moderate 34093
33.5%
strict 33856
33.3%
flexible 33825
33.2%

Most occurring characters

ValueCountFrequency (%)
e 135836
18.2%
t 101805
13.6%
r 67949
9.1%
i 67681
9.1%
l 67650
9.1%
m 34093
 
4.6%
o 34093
 
4.6%
d 34093
 
4.6%
a 34093
 
4.6%
s 33856
 
4.5%
Other values (4) 135331
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 746480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 135836
18.2%
t 101805
13.6%
r 67949
9.1%
i 67681
9.1%
l 67650
9.1%
m 34093
 
4.6%
o 34093
 
4.6%
d 34093
 
4.6%
a 34093
 
4.6%
s 33856
 
4.5%
Other values (4) 135331
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 746480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 135836
18.2%
t 101805
13.6%
r 67949
9.1%
i 67681
9.1%
l 67650
9.1%
m 34093
 
4.6%
o 34093
 
4.6%
d 34093
 
4.6%
a 34093
 
4.6%
s 33856
 
4.5%
Other values (4) 135331
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 746480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 135836
18.2%
t 101805
13.6%
r 67949
9.1%
i 67681
9.1%
l 67650
9.1%
m 34093
 
4.6%
o 34093
 
4.6%
d 34093
 
4.6%
a 34093
 
4.6%
s 33856
 
4.5%
Other values (4) 135331
18.1%

room type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Entire home/apt
53285 
Private room
46173 
Shared room
 
2200
Hotel room
 
116

Length

Max length15
Median length15
Mean length13.54679
Min length10

Characters and Unicode

Total characters1378711
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 53285
52.4%
Private room 46173
45.4%
Shared room 2200
 
2.2%
Hotel room 116
 
0.1%

Length

2024-11-13T10:31:43.458514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T10:31:43.641901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
entire 53285
26.2%
home/apt 53285
26.2%
room 48489
23.8%
private 46173
22.7%
shared 2200
 
1.1%
hotel 116
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 155059
11.2%
t 152859
11.1%
o 150379
10.9%
r 150147
10.9%
m 101774
 
7.4%
101774
 
7.4%
a 101658
 
7.4%
i 99458
 
7.2%
h 55485
 
4.0%
p 53285
 
3.9%
Other values (9) 256833
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1378711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 155059
11.2%
t 152859
11.1%
o 150379
10.9%
r 150147
10.9%
m 101774
 
7.4%
101774
 
7.4%
a 101658
 
7.4%
i 99458
 
7.2%
h 55485
 
4.0%
p 53285
 
3.9%
Other values (9) 256833
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1378711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 155059
11.2%
t 152859
11.1%
o 150379
10.9%
r 150147
10.9%
m 101774
 
7.4%
101774
 
7.4%
a 101658
 
7.4%
i 99458
 
7.2%
h 55485
 
4.0%
p 53285
 
3.9%
Other values (9) 256833
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1378711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 155059
11.2%
t 152859
11.1%
o 150379
10.9%
r 150147
10.9%
m 101774
 
7.4%
101774
 
7.4%
a 101658
 
7.4%
i 99458
 
7.2%
h 55485
 
4.0%
p 53285
 
3.9%
Other values (9) 256833
18.6%

Construction year
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.4682
Minimum2003
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:43.849720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12007
median2012
Q32017
95-th percentile2021
Maximum2022
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7729744
Coefficient of variation (CV)0.0028686041
Kurtosis-1.2076975
Mean2012.4682
Median Absolute Deviation (MAD)5
Skewness0.007368754
Sum2.0481694 × 108
Variance33.327234
MonotonicityNot monotonic
2024-11-13T10:31:44.122917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2003 5262
 
5.2%
2014 5207
 
5.1%
2008 5193
 
5.1%
2006 5177
 
5.1%
2009 5142
 
5.1%
2019 5141
 
5.1%
2010 5126
 
5.0%
2020 5122
 
5.0%
2005 5099
 
5.0%
2012 5091
 
5.0%
Other values (10) 50214
49.3%
ValueCountFrequency (%)
2003 5262
5.2%
2004 4997
4.9%
2005 5099
5.0%
2006 5177
5.1%
2007 5059
5.0%
2008 5193
5.1%
2009 5142
5.1%
2010 5126
5.0%
2011 5028
4.9%
2012 5091
5.0%
ValueCountFrequency (%)
2022 5082
5.0%
2021 5005
4.9%
2020 5122
5.0%
2019 5141
5.1%
2018 5020
4.9%
2017 5022
4.9%
2016 4973
4.9%
2015 5060
5.0%
2014 5207
5.1%
2013 4968
4.9%

price
Real number (ℝ)

Distinct1151
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.04903
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:44.389770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.057
Q1137
median426
Q3710
95-th percentile942
Maximum999
Range998
Interquartile range (IQR)573

Descriptive statistics

Standard deviation318.22621
Coefficient of variation (CV)0.73485029
Kurtosis-1.2832406
Mean433.04903
Median Absolute Deviation (MAD)287
Skewness0.10777044
Sum44073132
Variance101267.92
MonotonicityNot monotonic
2024-11-13T10:31:44.663244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206 136
 
0.1%
481 129
 
0.1%
833 128
 
0.1%
1.056 128
 
0.1%
573 126
 
0.1%
972 125
 
0.1%
138 122
 
0.1%
283 120
 
0.1%
406 120
 
0.1%
430 120
 
0.1%
Other values (1141) 100520
98.8%
ValueCountFrequency (%)
1 105
0.1%
1.001 96
0.1%
1.002 86
0.1%
1.003 105
0.1%
1.004 99
0.1%
1.005 80
0.1%
1.006 107
0.1%
1.007 102
0.1%
1.008 86
0.1%
1.009 72
0.1%
ValueCountFrequency (%)
999 69
0.1%
998 94
0.1%
997 97
0.1%
996 66
0.1%
995 111
0.1%
994 90
0.1%
993 94
0.1%
992 89
0.1%
991 76
0.1%
990 86
0.1%

service fee
Real number (ℝ)

Distinct232
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.75243
Minimum0
Maximum240
Zeros235
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:44.927734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q167
median125
Q3182
95-th percentile229
Maximum240
Range240
Interquartile range (IQR)115

Descriptive statistics

Standard deviation66.539003
Coefficient of variation (CV)0.53336838
Kurtosis-1.1905398
Mean124.75243
Median Absolute Deviation (MAD)57
Skewness-0.00054629071
Sum12696554
Variance4427.4389
MonotonicityNot monotonic
2024-11-13T10:31:45.154551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 520
 
0.5%
216 519
 
0.5%
81 516
 
0.5%
177 515
 
0.5%
57 513
 
0.5%
127 503
 
0.5%
207 502
 
0.5%
128 500
 
0.5%
92 494
 
0.5%
178 493
 
0.5%
Other values (222) 96699
95.0%
ValueCountFrequency (%)
0 235
0.2%
10 264
0.3%
11 424
0.4%
12 428
0.4%
13 428
0.4%
14 442
0.4%
15 447
0.4%
16 492
0.5%
17 413
0.4%
18 426
0.4%
ValueCountFrequency (%)
240 246
0.2%
239 396
0.4%
238 458
0.5%
237 465
0.5%
236 423
0.4%
235 439
0.4%
234 462
0.5%
233 435
0.4%
232 419
0.4%
231 455
0.4%

minimum nights
Real number (ℝ)

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2292825
Minimum0
Maximum60
Zeros389
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:45.360741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q35
95-th percentile30
Maximum60
Range60
Interquartile range (IQR)4

Descriptive statistics

Standard deviation10.909465
Coefficient of variation (CV)1.5090661
Kurtosis5.1074917
Mean7.2292825
Median Absolute Deviation (MAD)2
Skewness2.2852157
Sum735753
Variance119.01643
MonotonicityNot monotonic
2024-11-13T10:31:45.597968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25211
24.8%
2 23453
23.0%
3 15995
15.7%
30 11569
11.4%
4 6572
 
6.5%
5 6000
 
5.9%
7 4001
 
3.9%
6 1522
 
1.5%
14 1065
 
1.0%
60 968
 
1.0%
Other values (44) 5418
 
5.3%
ValueCountFrequency (%)
0 389
 
0.4%
1 25211
24.8%
2 23453
23.0%
3 15995
15.7%
4 6572
 
6.5%
5 6000
 
5.9%
6 1522
 
1.5%
7 4001
 
3.9%
8 245
 
0.2%
9 156
 
0.2%
ValueCountFrequency (%)
60 968
1.0%
59 19
 
< 0.1%
58 2
 
< 0.1%
57 1
 
< 0.1%
56 3
 
< 0.1%
55 10
 
< 0.1%
53 2
 
< 0.1%
50 33
 
< 0.1%
48 1
 
< 0.1%
47 6
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing296
Missing (%)0.3%
Memory size1.6 MiB
5.0
23173 
4.0
23139 
3.0
23077 
2.0
22950 
1.0
9139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters304434
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row5.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0 23173
22.8%
4.0 23139
22.7%
3.0 23077
22.7%
2.0 22950
22.5%
1.0 9139
 
9.0%
(Missing) 296
 
0.3%

Length

2024-11-13T10:31:45.785222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T10:31:45.931964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5.0 23173
22.8%
4.0 23139
22.8%
3.0 23077
22.7%
2.0 22950
22.6%
1.0 9139
 
9.0%

Most occurring characters

ValueCountFrequency (%)
. 101478
33.3%
0 101478
33.3%
5 23173
 
7.6%
4 23139
 
7.6%
3 23077
 
7.6%
2 22950
 
7.5%
1 9139
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 304434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 101478
33.3%
0 101478
33.3%
5 23173
 
7.6%
4 23139
 
7.6%
3 23077
 
7.6%
2 22950
 
7.5%
1 9139
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 304434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 101478
33.3%
0 101478
33.3%
5 23173
 
7.6%
4 23139
 
7.6%
3 23077
 
7.6%
2 22950
 
7.5%
1 9139
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 304434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 101478
33.3%
0 101478
33.3%
5 23173
 
7.6%
4 23139
 
7.6%
3 23077
 
7.6%
2 22950
 
7.5%
1 9139
 
3.0%
Distinct78
Distinct (%)0.1%
Missing319
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean7.9505495
Minimum1
Maximum332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:46.111185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile29
Maximum332
Range331
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.281773
Coefficient of variation (CV)4.0603197
Kurtosis58.577793
Mean7.9505495
Median Absolute Deviation (MAD)0
Skewness7.2157839
Sum806623
Variance1042.1128
MonotonicityNot monotonic
2024-11-13T10:31:46.347765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 62934
61.8%
2 14315
 
14.1%
3 6527
 
6.4%
4 3526
 
3.5%
5 1974
 
1.9%
6 1456
 
1.4%
7 984
 
1.0%
8 953
 
0.9%
9 540
 
0.5%
10 513
 
0.5%
Other values (68) 7733
 
7.6%
ValueCountFrequency (%)
1 62934
61.8%
2 14315
 
14.1%
3 6527
 
6.4%
4 3526
 
3.5%
5 1974
 
1.9%
6 1456
 
1.4%
7 984
 
1.0%
8 953
 
0.9%
9 540
 
0.5%
10 513
 
0.5%
ValueCountFrequency (%)
332 41
 
< 0.1%
327 469
0.5%
232 271
0.3%
218 26
 
< 0.1%
208 204
0.2%
186 151
 
0.1%
171 61
 
0.1%
161 115
 
0.1%
152 55
 
0.1%
126 53
 
0.1%

availability 365
Real number (ℝ)

ZEROS 

Distinct366
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.22674
Minimum0
Maximum365
Zeros23902
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2024-11-13T10:31:46.533804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median96
Q3268
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)265

Descriptive statistics

Standard deviation133.42568
Coefficient of variation (CV)0.95149957
Kurtosis-1.3286909
Mean140.22674
Median Absolute Deviation (MAD)96
Skewness0.44747879
Sum14271436
Variance17802.412
MonotonicityNot monotonic
2024-11-13T10:31:46.757983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23902
 
23.5%
365 5252
 
5.2%
364 1162
 
1.1%
89 749
 
0.7%
1 733
 
0.7%
179 685
 
0.7%
90 674
 
0.7%
5 625
 
0.6%
3 576
 
0.6%
180 541
 
0.5%
Other values (356) 66875
65.7%
ValueCountFrequency (%)
0 23902
23.5%
1 733
 
0.7%
2 500
 
0.5%
3 576
 
0.6%
4 438
 
0.4%
5 625
 
0.6%
6 428
 
0.4%
7 416
 
0.4%
8 431
 
0.4%
9 391
 
0.4%
ValueCountFrequency (%)
365 5252
5.2%
364 1162
 
1.1%
363 502
 
0.5%
362 384
 
0.4%
361 288
 
0.3%
360 245
 
0.2%
359 278
 
0.3%
358 387
 
0.4%
357 252
 
0.2%
356 219
 
0.2%

Interactions

2024-11-13T10:31:31.608987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:15.617243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:17.365033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:19.209952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:20.824598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:22.564922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:24.629504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:26.421193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:28.080273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:29.980554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:31.775153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:15.785258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:17.541317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:19.380902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:20.996605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:22.793883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:24.774020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:26.597808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:28.298049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:30.141565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:31.936211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:15.924214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:17.725603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:19.532075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:21.160975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:22.999881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:24.926213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:26.766224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:28.483974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:30.324922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:32.090219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:16.074824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:17.899950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:19.707392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:21.315767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:23.194927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:25.068966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:26.927711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:28.655185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:30.486579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:32.398565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:16.222832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:18.071732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:19.853537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:21.471653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:23.410630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:25.212498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:27.101081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:28.855811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:30.651002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:32.574358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:16.402898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:18.277483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:20.039442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:21.642277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:23.604408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:25.419305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:27.266146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:29.148551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:30.823436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:32.799765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:16.552232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:18.451997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:20.200383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:21.819088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:23.764158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:25.572706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:27.418241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:29.333490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:30.974821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:32.952104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:16.704091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:18.625039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:20.348705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:21.983538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:23.955335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:25.765957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:27.564770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:29.515398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:31.125776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:33.109236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:16.991526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:18.805763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:20.501024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:22.217084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:24.133074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:26.034178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:27.717708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:29.670629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:31.288349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:33.295271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:17.137875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:19.019625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:20.675641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:22.384362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:24.292338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:26.226836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:27.891333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:29.823274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-13T10:31:31.447278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-11-13T10:31:33.578807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-13T10:31:34.150038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-13T10:31:34.677744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlonginstant_bookablecancellation_policyroom typeConstruction yearpriceservice feeminimum nightsreview rate numbercalculated host listings countavailability 365
01001254Clean & quiet apt home by the park80014485718unconfirmedMadalineBrooklynKensington40.64749-73.97237FalsestrictPrivate room2020.0966.000193.010.04.06.0286.0
11002102Skylit Midtown Castle52335172823verifiedJennaManhattanMidtown40.75362-73.98377FalsemoderateEntire home/apt2007.0142.00028.030.04.02.0228.0
21002403THE VILLAGE OF HARLEM....NEW YORK !78829239556unconfirmedEliseManhattanHarlem40.80902-73.94190TrueflexiblePrivate room2005.0620.000124.03.05.01.0352.0
31002755NaN85098326012unconfirmedGarryBrooklynClinton Hill40.68514-73.95976TruemoderateEntire home/apt2005.0368.00074.030.04.01.0322.0
41003689Entire Apt: Spacious Studio/Loft by central park92037596077verifiedLyndonManhattanEast Harlem40.79851-73.94399FalsemoderateEntire home/apt2009.0204.00041.010.03.01.0289.0
51004098Large Cozy 1 BR Apartment In Midtown East45498551794verifiedMichelleManhattanMurray Hill40.74767-73.97500TrueflexibleEntire home/apt2013.0577.000115.03.03.01.0365.0
61004650BlissArtsSpace!61300605564unconfirmedAlbertaBrooklynBedford-Stuyvesant40.68688-73.95596FalsemoderatePrivate room2015.071.00014.045.05.01.0224.0
71005202BlissArtsSpace!90821839709unconfirmedEmmaBrooklynBedford-Stuyvesant40.68688-73.95596FalsemoderatePrivate room2009.01.060212.045.05.01.0219.0
81005754Large Furnished Room Near B'way79384379533verifiedEvelynManhattanHell's Kitchen40.76489-73.98493TruestrictPrivate room2005.01.018204.02.03.01.0180.0
91006307Cozy Clean Guest Room - Family Apt75527839483unconfirmedCarlManhattanUpper West Side40.80178-73.96723FalsestrictPrivate room2015.0291.00058.02.05.01.0365.0
idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlonginstant_bookablecancellation_policyroom typeConstruction yearpriceservice feeminimum nightsreview rate numbercalculated host listings countavailability 365
1025896089676Lrg room 1 block from Prospect Park74549151787unconfirmedDaveBrooklynFlatbush40.65231-73.96189FalseflexiblePrivate room2006.0306.00061.03.01.01.0200.0
1025906090228Wonderful artists' loft in Brooklyn9184535139unconfirmedDanielBrooklynCrown Heights40.66673-73.96127TruemoderateEntire home/apt2003.0250.00050.01.01.01.0276.0
1025916090781Columbus Ave Apt 1 block from Park50908010324verifiedLawrenceManhattanUpper West Side40.77408-73.98181FalsestrictEntire home/apt2005.01.139228.05.05.01.0134.0
10259260913333BR/1 Ba in TriBeCa w/ outdoor deck53266862889unconfirmedNickManhattanTribeca40.71845-74.01183FalsemoderateEntire home/apt2016.0787.000157.01.02.01.0177.0
1025936091885Welcoming, Clean, Cheap on St Marks33188605074verifiedFelipeManhattanEast Village40.72826-73.98422TruestrictPrivate room2017.01.099220.01.04.02.0152.0
1025946092437Spare room in Williamsburg12312296767verifiedKrikBrooklynWilliamsburg40.70862-73.94651FalseflexiblePrivate room2003.0844.000169.01.03.01.0227.0
1025956092990Best Location near Columbia U77864383453unconfirmedMifanManhattanMorningside Heights40.80460-73.96545TruemoderatePrivate room2016.0837.000167.01.02.02.0365.0
1025966093542Comfy, bright room in Brooklyn69050334417unconfirmedMeganBrooklynPark Slope40.67505-73.98045TruemoderatePrivate room2009.0988.000198.03.05.01.0342.0
1025976094094Big Studio-One Stop from Midtown11160591270unconfirmedChristopherQueensLong Island City40.74989-73.93777TruestrictEntire home/apt2015.0546.000109.02.03.01.0365.0
1025986094647585 sf Luxury Studio68170633372unconfirmedRebeccaManhattanUpper West Side40.76807-73.98342FalseflexibleEntire home/apt2010.01.032206.01.03.01.069.0

Duplicate rows

Most frequently occurring

idNAMEhost idhost_identity_verifiedhost nameneighbourhood groupneighbourhoodlatlonginstant_bookablecancellation_policyroom typeConstruction yearpriceservice feeminimum nightsreview rate numbercalculated host listings countavailability 365# duplicates
06026161Upper East Side 2 bedroom- close to Hospitals-65193709566verifiedJulianaManhattanUpper East Side40.76222-73.96030FalsemoderateEntire home/apt2008.0105.021.030.03.034.0157.02
16026714Close to East Side Hospitals- Modern 2 Bedroom Apt31072202372verifiedJulianaManhattanUpper East Side40.76249-73.96217FalsemoderateEntire home/apt2008.0285.057.030.03.034.067.02
26027266ACADIA Spacious 2 Bedroom Apt - Close to Hospitals95854111798verifiedJulianaManhattanUpper East Side40.76021-73.96157FalsemoderateEntire home/apt2014.0586.0117.030.05.034.0211.02
36027818*ENCHANTMENT* Upper East Side 2 bedroom- Sunny!73401481508unconfirmedJulianaManhattanUpper East Side40.76244-73.96031TruemoderateEntire home/apt2006.0539.0108.030.05.034.0365.02
46028371*JAMES* Amazing Spacious 2 Bedroom- Bright!37678424985verifiedJulianaManhattanUpper East Side40.76035-73.96133FalseflexibleEntire home/apt2021.0806.0161.030.04.034.0365.02
56028923Large Private Upper West Side Room48515749618verifiedMaxwellManhattanUpper West Side40.78567-73.97665FalseflexiblePrivate room2020.0479.096.01.03.01.0215.02
66029475*ODYSSEY* Sunny 1 Bedroom Apt- Bright & Cheery!50618412686unconfirmedJulianaManhattanUpper West Side40.78181-73.98466TruestrictEntire home/apt2017.0424.085.030.03.034.0129.02
76030028*WINDSONG* Serene 1 BR in Townhouse near Park60449613505unconfirmedJulianaManhattanUpper West Side40.78289-73.98477TruestrictEntire home/apt2020.0967.0193.030.04.034.0164.02
860305802BR Cozy, Large & Central Apartment45918690207unconfirmedPhilippeManhattanMidtown40.74462-73.98272TruestrictEntire home/apt2022.0419.084.02.03.01.0351.02
96031132Nice room in a super nice apartment14162979052unconfirmedTerryBrooklynWilliamsburg40.71591-73.94170FalsestrictPrivate room2008.0878.0176.07.03.01.0365.02